Word Aligner MCP
Server Details
Word Aligner exposes an MCP server so AI agents can turn a phrase and its translation into a shareable word-alignment diagram. The server runs over Streamable HTTP at aligner.tinygods.dev/mcp with no authentication and a single tool, create_word_alignment. An agent translates and tokenizes the text, works out which words correspond, calls the tool, and gets back a URL plus a preview image.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusion between tools. The agent will always select this tool for its intended purpose.
With a single tool, naming consistency is perfect. The tool name 'create_word_alignment' follows a clear verb_noun pattern and is self-explanatory.
One tool for a specific domain (word alignment) is borderline. While the tool is detailed and covers creation, a server with a broader scope would typically need more tools (e.g., for listing or managing diagrams). The count feels thin but not extremely mismatched.
The server only supports creating a word alignment diagram. There are no tools for listing, updating, or deleting diagrams, which are notable gaps if users need to manage multiple alignments. However, the creation tool itself is thorough, covering alignment specification and settings.
Available Tools
1 toolcreate_word_alignmentCreate word alignment diagramARead-onlyIdempotentInspect
Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image.
Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic), or build a Leipzig-style interlinear gloss.
Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices:
Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]).
The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots.
Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]).
In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order.
Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| lines | Yes | Text lines, top to bottom. Each entry is a plain string or an object with per-line visual options. | |
| pairs | No | Per-pair controls for a specific adjacent line pair. | |
| settings | No | Global visual overrides. Unset fields inherit defaults. | |
| alignments | No | Word-alignment links as [lineA, wordA, lineB, wordB] (0-based indices, lines must be adjacent). |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | The shareable diagram URL. Return this to the user exactly as received, character for character. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, destructiveHint. Description adds tokenization details, RTL handling, alignment constraints, and return format. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose, then usage, then detailed rules. Every sentence adds value; no redundancy. Well-organized for a complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers all aspects: tokenization, alignment format, visual options, RTL, and return. Output schema exists, so return values are handled. Comprehensive for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters. Description enriches with tokenization examples, default values (e.g., tokenSplitChars), and alignment index semantics, adding value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool creates a shareable Word Aligner diagram showing word matches across stacked lines, with specific use cases. It distinguishes itself as the only sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear when-to-use scenarios are provided (translations, interlinear glosses). No explicit when-not-to-use, but no alternative tools exist, so less critical. Tokenization rules and index semantics give practical guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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